A Need for Targeted Teaching of Shared Decision-Making as Identified from an Assessment of Client-Centered Communication Skills Training with Companion Animal Veterinarians
Bibliographic record
Abstract
Shared decision-making has been increasingly discussed as a communication practice within veterinary medicine, and it is gaining more traction for diagnostic and treatment planning conversations and specifically offering a spectrum of care. This teaching tip describes the data from an investigation of veterinarians’ shared decision-making in a pre-test/post-test communication skills training intervention that used a client-centered, skills-based communication approach. Practice teams from a purposive sample of four companion animal veterinary clinics in Texas participated in a 15-month communication skills intervention, including interactive group workshops and one-on-one communication coaching. To assess the outcome of the intervention, for nine participating veterinarians, appointments recorded pre- ( n = 85) and post-intervention ( n = 85) were analyzed using the Observer OPTION 5 instrument to assess shared decision-making. The intervention effect was evaluated using mixed logistic regression, adjusting for appointment type. The communication intervention did not significantly impact participating veterinarians’ demonstration of shared decision-making (pre = 25.42, n = 55; post = 28.03, n = 56; p = 0.36). Appointment type was significantly associated with veterinarians’ OPTION 5 scores ( p = .0004) and health problem appointments (OPTION 5 = 30.07) demonstrated greater shared decision-making than preventive care appointments (OPTION 5 = 22.81). Findings suggest that client-centered, skills-based training traditionally used in veterinary curricula and continuing education may not foster the use of shared decision-making, which is a higher-order communication approach that may require a dedicated process-oriented training. This teaching tip highlights the need for a targeted stepwise approach to teach shared decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".